Computer Science 273B: Kernel-Based Learning
This course introduces the students to one of the most influential developments in modern machine learning, namely kernel methods. The course will be focused on familiarizing the student with a number of practical kernel-based algorithms (such as “support vector machines”, “kernel Fisher Discrimination”, “kernel principal components analysis” and “Gaussian processes”) and a number of techniques to construct kernels (such as ANOVA kernels, string kernels, graph kernels, diffusion kernels, set kernels). The necessary learning-theoretic preliminaries will be treated as well but it will not be the focus of this course. Applications to real-world problems will serve as examples.
Author Welling, Max Title Professor of Computer Science Department Information and Computer Science
Max Welling, Professor of Computer Science, School of Information and Computer Sciences, University of California, Irvine
Computer Science 273B: Kernel-Based Learning by Professor Max Welling is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.